提出全景质量映射框架，实现无监督分割的像素级精准评估。
SegAssess: Panoramic quality mapping for robust and transferable unsupervised segmentation assessment
- 将分割质量评估转为四类像素分类任务，生成完整质量图。
- 在多个数据集上超越现有方法，零样本迁移能力显著提升。
- 适合遥感图像分割质量评估、无真值场景下的模型验证。
高质量图像分割是遥感中像素级地理空间分析的基础，尤其在缺乏真值的无监督设置下，需要鲁棒的分割质量评估（SQA）。尽管近期基于深度学习的无监督SQA方法展现出潜力，但普遍存在评估粒度粗、评估不完整、泛化能力差等问题。为此，本文提出全景质量映射（PQM）新范式，构建了名为SegAssess的深度学习框架实现该思路。SegAssess将SQA建模为细粒度四分类全景分割任务，将待评估分割掩码中的像素划分为真正例（TP）、假正例（FP）、真负例（TN）和假负例（FN），生成完整质量图。依托增强版Segment Anything Model（SAM）架构，通过交叉注意力机制利用输入掩码作为提示实现有效特征融合。关键创新包括：边缘引导压缩分支（EGC）结合聚合语义滤波模块（ASF），显著提升复杂边缘区域预测精度；以及融合多源掩码的增强混合采样（AMS）训练策略，大幅增强跨域鲁棒性与零样本迁移能力。大量实验表明，SegAssess达到当前最优性能，并展现出优异的零样本迁移能力。代码已开源：https://github.com/Yangbn97/SegAssess。
原文摘要 · Abstract (English)
High-quality image segmentation is fundamental to pixel-level geospatial analysis in remote sensing, necessitating robust segmentation quality assessment (SQA), particularly in unsupervised settings lacking ground truth. Although recent deep learning (DL) based unsupervised SQA methods show potential, they often suffer from coarse evaluation granularity, incomplete assessments, and poor transferability. To overcome these limitations, this paper introduces Panoramic Quality Mapping (PQM) as a new paradigm for comprehensive, pixel-wise SQA, and presents SegAssess, a novel deep learning framework realizing this approach. SegAssess distinctively formulates SQA as a fine-grained, four-class panoramic segmentation task, classifying pixels within a segmentation mask under evaluation into true positive (TP), false positive (FP), true negative (TN), and false negative (FN) categories, thereby generating a complete quality map. Leveraging an enhanced Segment Anything Model (SAM) architecture, SegAssess uniquely employs the input mask as a prompt for effective feature integration via cross-attention. Key innovations include an Edge Guided Compaction (EGC) branch with an Aggregated Semantic Filter (ASF) module to refine predictions near challenging object edges, and an Augmented Mixup Sampling (AMS) training strategy integrating multi-source masks to significantly boost cross-domain robustness and zero-shot transferability. Comprehensive experiments demonstrate that SegAssess achieves state-of-the-art (SOTA) performance and exhibits remarkable zero-shot transferability to unseen masks. The code is available at https://github.com/Yangbn97/SegAssess.
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